Search Results for author: Clare Voss

Found 39 papers, 1 papers with code

Builder, we have done it: Evaluating & Extending Dialogue-AMR NLU Pipeline for Two Collaborative Domains

no code implementations IWCS (ACL) 2021 Claire Bonial, Mitchell Abrams, David Traum, Clare Voss

We adopt, evaluate, and improve upon a two-step natural language understanding (NLU) pipeline that incrementally tames the variation of unconstrained natural language input and maps to executable robot behaviors.

AMR Parsing Natural Language Understanding

The Search for Agreement on Logical Fallacy Annotation of an Infodemic

no code implementations LREC 2022 Claire Bonial, Austin Blodgett, Taylor Hudson, Stephanie M. Lukin, Jeffrey Micher, Douglas Summers-Stay, Peter Sutor, Clare Voss

We evaluate an annotation schema for labeling logical fallacy types, originally developed for a crowd-sourcing annotation paradigm, now using an annotation paradigm of two trained linguist annotators.

Logical Fallacies

InfoForager: Leveraging Semantic Search with AMR for COVID-19 Research

no code implementations DMR (COLING) 2020 Claire Bonial, Stephanie M. Lukin, David Doughty, Steven Hill, Clare Voss

This paper examines how Abstract Meaning Representation (AMR) can be utilized for finding answers to research questions in medical scientific documents, in particular, to advance the study of UV (ultraviolet) inactivation of the novel coronavirus that causes the disease COVID-19.

Domain Adaptation

What Can a Generative Language Model Answer About a Passage?

no code implementations EMNLP (MRQA) 2021 Douglas Summers-Stay, Claire Bonial, Clare Voss

Generative language models trained on large, diverse corpora can answer questions about a passage by generating the most likely continuation of the passage followed by a question/answer pair.

Language Modelling

Navigating to Success in Multi-Modal Human-Robot Collaboration: Analysis and Corpus Release

no code implementations26 Oct 2023 Stephanie M. Lukin, Kimberly A. Pollard, Claire Bonial, Taylor Hudson, Ron Arstein, Clare Voss, David Traum

Human-guided robotic exploration is a useful approach to gathering information at remote locations, especially those that might be too risky, inhospitable, or inaccessible for humans.

The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction

1 code implementation EMNLP 2021 Manling Li, Sha Li, Zhenhailong Wang, Lifu Huang, Kyunghyun Cho, Heng Ji, Jiawei Han, Clare Voss

We introduce a new concept of Temporal Complex Event Schema: a graph-based schema representation that encompasses events, arguments, temporal connections and argument relations.

GAIA: A Fine-grained Multimedia Knowledge Extraction System

no code implementations ACL 2020 Manling Li, Alireza Zareian, Ying Lin, Xiaoman Pan, Spencer Whitehead, Brian Chen, Bo Wu, Heng Ji, Shih-Fu Chang, Clare Voss, Daniel Napierski, Marjorie Freedman

We present the first comprehensive, open source multimedia knowledge extraction system that takes a massive stream of unstructured, heterogeneous multimedia data from various sources and languages as input, and creates a coherent, structured knowledge base, indexing entities, relations, and events, following a rich, fine-grained ontology.

Dialogue-AMR: Abstract Meaning Representation for Dialogue

no code implementations LREC 2020 Claire Bonial, Lucia Donatelli, Mitchell Abrams, Stephanie M. Lukin, Stephen Tratz, Matthew Marge, Ron artstein, David Traum, Clare Voss

This paper describes a schema that enriches Abstract Meaning Representation (AMR) in order to provide a semantic representation for facilitating Natural Language Understanding (NLU) in dialogue systems.

Natural Language Understanding

Cross-lingual Structure Transfer for Relation and Event Extraction

no code implementations IJCNLP 2019 Ananya Subburathinam, Di Lu, Heng Ji, Jonathan May, Shih-Fu Chang, Avirup Sil, Clare Voss

The identification of complex semantic structures such as events and entity relations, already a challenging Information Extraction task, is doubly difficult from sources written in under-resourced and under-annotated languages.

Event Extraction Relation +1

Augmenting Abstract Meaning Representation for Human-Robot Dialogue

no code implementations WS 2019 Claire Bonial, Lucia Donatelli, Stephanie M. Lukin, Stephen Tratz, Ron artstein, David Traum, Clare Voss

We detail refinements made to Abstract Meaning Representation (AMR) that make the representation more suitable for supporting a situated dialogue system, where a human remotely controls a robot for purposes of search and rescue and reconnaissance.

Multilingual Entity, Relation, Event and Human Value Extraction

no code implementations NAACL 2019 Manling Li, Ying Lin, Joseph Hoover, Spencer Whitehead, Clare Voss, Morteza Dehghani, Heng Ji

This paper demonstrates a state-of-the-art end-to-end multilingual (English, Russian, and Ukrainian) knowledge extraction system that can perform entity discovery and linking, relation extraction, event extraction, and coreference.

Event Extraction Relation +1

Incorporating Background Knowledge into Video Description Generation

no code implementations EMNLP 2018 Spencer Whitehead, Heng Ji, Mohit Bansal, Shih-Fu Chang, Clare Voss

We develop an approach that uses video meta-data to retrieve topically related news documents for a video and extracts the events and named entities from these documents.

Decoder Text Generation +2

Towards a Computational Lexicon for Moroccan Darija: Words, Idioms, and Constructions

no code implementations COLING 2018 Jamal Laoudi, Claire Bonial, Lucia Donatelli, Stephen Tratz, Clare Voss

In this paper, we explore the challenges of building a computational lexicon for Moroccan Darija (MD), an Arabic dialect spoken by over 32 million people worldwide but which only recently has begun appearing frequently in written form in social media.

Machine Translation

Can You Spot the Semantic Predicate in this Video?

no code implementations COLING 2018 Christopher Reale, Claire Bonial, Heesung Kwon, Clare Voss

We propose a method to improve human activity recognition in video by leveraging semantic information about the target activities from an expert-defined linguistic resource, VerbNet.

Human Activity Recognition Multi-Task Learning

STYLUS: A Resource for Systematically Derived Language Usage

no code implementations COLING 2018 Bonnie Dorr, Clare Voss

We describe a resource derived through extraction of a set of argument realizations from an existing lexical-conceptual structure (LCS) Verb Database of 500 verb classes (containing a total of 9525 verb entries) to include information about realization of arguments for a range of different verb classes.

Dialogue Management Robot Navigation +1

The Case for Systematically Derived Spatial Language Usage

no code implementations WS 2018 Bonnie Dorr, Clare Voss

This position paper argues that, while prior work in spatial language understanding for tasks such as robot navigation focuses on mapping natural language into deep conceptual or non-linguistic representations, it is possible to systematically derive regular patterns of spatial language usage from existing lexical-semantic resources.

Position Robot Navigation

Object and Text-guided Semantics for CNN-based Activity Recognition

no code implementations4 May 2018 Sungmin Eum, Christopher Reale, Heesung Kwon, Claire Bonial, Clare Voss

We further improve upon the multitask learning approach by exploiting a text-guided semantic space to select the most relevant objects with respect to the target activities.

Human Activity Recognition Object Recognition

Applying the Wizard-of-Oz Technique to Multimodal Human-Robot Dialogue

no code implementations10 Mar 2017 Matthew Marge, Claire Bonial, Brendan Byrne, Taylor Cassidy, A. William Evans, Susan G. Hill, Clare Voss

Our overall program objective is to provide more natural ways for soldiers to interact and communicate with robots, much like how soldiers communicate with other soldiers today.

Dialogue Management Management +1

Using a Distributional Semantic Vector Space with a Knowledge Base for Reasoning in Uncertain Conditions

no code implementations13 Jun 2016 Douglas Summers-Stay, Clare Voss, Taylor Cassidy

The inherent inflexibility and incompleteness of commonsense knowledge bases (KB) has limited their usefulness.

Scalable Topical Phrase Mining from Text Corpora

no code implementations24 Jun 2014 Ahmed El-Kishky, Yanglei Song, Chi Wang, Clare Voss, Jiawei Han

Our solution combines a novel phrase mining framework to segment a document into single and multi-word phrases, and a new topic model that operates on the induced document partition.

Topic Models

Finding Romanized Arabic Dialect in Code-Mixed Tweets

no code implementations LREC 2014 Clare Voss, Stephen Tratz, Jamal Laoudi, Douglas Briesch

Recent computational work on Arabic dialect identification has focused primarily on building and annotating corpora written in Arabic script.

Dialect Identification

Assessing Divergence Measures for Automated Document Routing in an Adaptive MT System

no code implementations LREC 2012 Claire Jaja, Douglas Briesch, Jamal Laoudi, Clare Voss

This paper investigates the use of the Jensen-Shannon divergence measure for automatically routing new documents within a translation system with multiple MT engines to the appropriate custom MT engine in order to obtain the best translation.

Document Classification Machine Translation +1

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